import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      LxmertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      LxmertForPreTraining(UpperCamelCase_       )

 # Load weights from tf checkpoint
 load_tf_weights_in_lxmert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--config_file""",
         default=None,
         type=str,
         required=True,
         help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

_SCREAMING_SNAKE_CASE 		=      """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""





def 			lowercase(	)		->	None:







 '''simple docstring'''
 UpperCamelCase			  =      input("""Enter message: """       )
 UpperCamelCase			  =      input("""Enter key [alphanumeric]: """       )
 UpperCamelCase			  =      input("""Encrypt/Decrypt [e/d]: """       )

 if mode.lower().startswith("""e"""       ):
  UpperCamelCase			  =      """encrypt"""
  UpperCamelCase			  =      encrypt_message(UpperCamelCase_							,					UpperCamelCase_       )
 elif mode.lower().startswith("""d"""       ):
  UpperCamelCase			  =      """decrypt"""
  UpperCamelCase			  =      decrypt_message(UpperCamelCase_							,					UpperCamelCase_       )

 print(f"""\n{mode.title()}ed message:"""       )
 print(UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 return translate_message(UpperCamelCase_							,					UpperCamelCase_							,					"""encrypt"""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 return translate_message(UpperCamelCase_							,					UpperCamelCase_							,					"""decrypt"""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 UpperCamelCase			  =      []
 UpperCamelCase			  =      0
 UpperCamelCase			  =      key.upper()

 for symbol in message:
  UpperCamelCase			  =      LETTERS.find(symbol.upper()       )
  if num != -1:
   if mode == "encrypt":
    num += LETTERS.find(key[key_index]       )
   elif mode == "decrypt":
    num -= LETTERS.find(key[key_index]       )

   num %= len(UpperCamelCase_       )

   if symbol.isupper():
    translated.append(LETTERS[num]       )
   elif symbol.islower():
    translated.append(LETTERS[num].lower()       )

   key_index += 1
   if key_index == len(UpperCamelCase_       ):
    UpperCamelCase			  =      0
  else:
   translated.append(UpperCamelCase_       )
 return "".join(UpperCamelCase_       )


if __name__ == "__main__":
     main()
